InterEvDock3: a combined template-based and free docking server with increased performance through explicit modeling of complex homologs and integration of covariation-based contact maps

Neutrophils disrupt B-1a cell homeostasis by targeting Siglec-G to exacerbate sepsis

B-1a cells, an innate-like cell population, are crucial for pathogen defense and the regulation of inflammation through their release of natural IgM and IL-10. In sepsis, B-1a cell numbers are decreased in the peritoneal cavity as they robustly migrate to the spleen. Within the spleen, migrating B-1a cells differentiate into plasma cells, leading to alterations in their original phenotype and functionality. We discovered a key player, sialic acid-binding immunoglobulin-like lectin-G (Siglec-G), which is expressed predominantly on B-1a cells and negatively regulates B-1a cell migration to maintain homeostasis. Siglec-G interacts with CXCR4/CXCL12 to modulate B-1a cell migration. Neutrophils aid B-1a cell migration via neutrophil elastase (NE)-mediated Siglec-G cleavage. Human studies revealed increased NE expression in septic patients. We identified an NE cleavage sequence in silico, leading to the discovery of a decoy peptide that protects Siglec-G, preserves peritoneal B-1a cells, reduces inflammation, and enhances sepsis survival. The role of Siglec-G in inhibiting B-1a cell migration to maintain their inherent phenotype and function is compromised by NE in sepsis, offering valuable insights into B-1a cell homeostasis. Employing a small decoy peptide to prevent NE-mediated Siglec-G cleavage has emerged as a promising strategy to sustain peritoneal B-1a cell homeostasis, alleviate inflammation, and ultimately improve outcomes in sepsis patients.

Mitigation of TDP-43 toxic phenotype by an RGNEF fragment in amyotrophic lateral sclerosis models

Aggregation of the RNA-binding protein TAR DNA binding protein (TDP-43) is a hallmark of TDP-proteinopathies including amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD). As TDP-43 aggregation and dysregulation are causative of neuronal death, there is a special interest in targeting this protein as a therapeutic approach. Previously, we found that TDP-43 extensively co-aggregated with the dual function protein GEF (guanine exchange factor) and RNA-binding protein rho guanine nucleotide exchange factor (RGNEF) in ALS patients. Here, we show that an N-terminal fragment of RGNEF (NF242) interacts directly with the RNA recognition motifs of TDP-43 competing with RNA and that the IPT/TIG domain of NF242 is essential for this interaction. Genetic expression of NF242 in a fruit fly ALS model overexpressing TDP-43 suppressed the neuropathological phenotype increasing lifespan, abolishing motor defects and preventing neurodegeneration. Intracerebroventricular injections of AAV9/NF242 in a severe TDP-43 murine model (rNLS8) improved lifespan and motor phenotype, and decreased neuroinflammation markers. Our results demonstrate an innovative way to target TDP-43 proteinopathies using a protein fragment with a strong affinity for TDP-43 aggregates and a mechanism that includes competition with RNA sequestration, suggesting a promising therapeutic strategy for TDP-43 proteinopathies such as ALS and FTD.

ZEPPI: Proteome-scale sequence-based evaluation of protein-protein interaction models

We introduce ZEPPI (Z-score Evaluation of Protein-Protein Interfaces), a framework to evaluate structural models of a complex based on sequence coevolution and conservation involving residues in protein-protein interfaces. The ZEPPI score is calculated by comparing metrics for an interface to those obtained from randomly chosen residues. Since contacting residues are defined by the structural model, this obviates the need to account for indirect interactions. Further, although ZEPPI relies on species-paired multiple sequence alignments, its focus on interfacial residues allows it to leverage quite shallow alignments. ZEPPI can be implemented on a proteome-wide scale and is applied here to millions of structural models of dimeric complexes in the Escherichia coli and human interactomes found in the PrePPI database. PrePPI's scoring function is based primarily on the evaluation of protein-protein interfaces, and ZEPPI adds a new feature to this analysis through the incorporation of evolutionary information. ZEPPI performance is evaluated through applications to experimentally determined complexes and to decoys from the CASP-CAPRI experiment. As we discuss, the standard CAPRI scores used to evaluate docking models are based on model quality and not on the ability to give yes/no answers as to whether two proteins interact. ZEPPI is able to detect weak signals from PPI models that the CAPRI scores define as incorrect and, similarly, to identify potential PPIs defined as low confidence by the current PrePPI scoring function. A number of examples that illustrate how the combination of PrePPI and ZEPPI can yield functional hypotheses are provided.

Senktide blocks aberrant RTN3 interactome to retard memory decline and tau pathology in social isolated Alzheimer's disease mice

Sporadic or late-onset Alzheimer's disease (LOAD) accounts for more than 95% of Alzheimer's disease (AD) cases without any family history. Although genome-wide association studies have identified associated risk genes and loci for LOAD, numerous studies suggest that many adverse environmental factors, such as social isolation, are associated with an increased risk of dementia. However, the underlying mechanisms of social isolation in AD progression remain elusive. In the current study, we found that 7 days of social isolation could trigger pattern separation impairments and presynaptic abnormalities of the mossy fibre-CA3 circuit in AD mice. We also revealed that social isolation disrupted histone acetylation and resulted in the downregulation of 2 dentate gyrus (DG)-enriched miRNAs, which simultaneously target reticulon 3 (RTN3), an endoplasmic reticulum protein that aggregates in presynaptic regions to disturb the formation of functional mossy fibre boutons (MFBs) by recruiting multiple mitochondrial and vesicle-related proteins. Interestingly, the aggregation of RTN3 also recruits the PP2A B subunits to suppress PP2A activity and induce tau hyperphosphorylation, which, in turn, further elevates RTN3 and forms a vicious cycle. Finally, using an artificial intelligence-assisted molecular docking approach, we determined that senktide, a selective agonist of neurokinin3 receptors (NK3R), could reduce the binding of RTN3 with its partners. Moreover, application of senktide in vivo effectively restored DG circuit disorders in socially isolated AD mice. Taken together, our findings not only demonstrate the epigenetic regulatory mechanism underlying mossy fibre synaptic disorders orchestrated by social isolation and tau pathology but also reveal a novel potential therapeutic strategy for AD.

A Perspective on the Prospective Use of AI in Protein Structure Prediction

AlphaFold2 (AF2) and RoseTTaFold (RF) have revolutionized structural biology, serving as highly reliable and effective methods for predicting protein structures. This article explores their impact and limitations, focusing on their integration into experimental pipelines and their application in diverse protein classes, including membrane proteins, intrinsically disordered proteins (IDPs), and oligomers. In experimental pipelines, AF2 models help X-ray crystallography in resolving the phase problem, while complementarity with mass spectrometry and NMR data enhances structure determination and protein flexibility prediction. Predicting the structure of membrane proteins remains challenging for both AF2 and RF due to difficulties in capturing conformational ensembles and interactions with the membrane. Improvements in incorporating membrane-specific features and predicting the structural effect of mutations are crucial. For intrinsically disordered proteins, AF2's confidence score (pLDDT) serves as a competitive disorder predictor, but integrative approaches including molecular dynamics (MD) simulations or hydrophobic cluster analyses are advocated for accurate dynamics representation. AF2 and RF show promising results for oligomeric models, outperforming traditional docking methods, with AlphaFold-Multimer showing improved performance. However, some caveats remain in particular for membrane proteins. Real-life examples demonstrate AF2's predictive capabilities in unknown protein structures, but models should be evaluated for their agreement with experimental data. Furthermore, AF2 models can be used complementarily with MD simulations. In this Perspective, we propose a "wish list" for improving deep-learning-based protein folding prediction models, including using experimental data as constraints and modifying models with binding partners or post-translational modifications. Additionally, a meta-tool for ranking and suggesting composite models is suggested, driving future advancements in this rapidly evolving field.

GRAMM Web Server for Protein Docking

Prediction of the structure of protein complexes by docking methods is a well-established research field. The intermolecular energy landscapes in protein-protein interactions can be used to refine docking predictions and to detect macro-characteristics, such as the binding funnel. A new GRAMM web server for protein docking predicts a spectrum of docking poses that characterize the intermolecular energy landscape in protein interaction. A user-friendly interface provides options to choose free or template-based docking, as well as other advanced features, such as clustering of the docking poses, and interactive visualization of the docked models.

Free-Docking and Template-Based Docking: Physics Versus Knowledge-Based Docking

Docking methods can be used to predict the orientations of two or more molecules with respect of each other using a plethora of various algorithms, which can be based on the physics of interactions or can use information from databases and templates. The usability of these approaches depends on the type and size of the molecules, whose relative orientation will be estimated. The two most important limitations are (i) the computational cost of the prediction and (ii) the availability of the structural information for similar complexes. In general, if there is enough information about similar systems, knowledge-based and template-based methods can significantly reduce the computational cost while providing high accuracy of the prediction. However, if the information about the system topology and interactions between its partners is scarce, physics-based methods are more reliable or even the only choice. In this chapter, knowledge-, template-, and physics-based methods will be compared and briefly discussed providing examples of their usability with a special emphasis on physics-based protein-protein, protein-peptide, and protein-fullerene docking in the UNRES coarse-grained model.

ABCE1 selectively promotes HIF-1α transactivation of angiogenic gene expression

BACKGROUND

Copper (Cu), by inhibiting the factor inhibiting HIF-1 (FIH-1), promotes the transcriptional activity of hypoxia-inducible factor-1 (HIF-1).

OBJECTIVE

The present study was undertaken to understand the molecular mechanism by which Cu inhibits FIH-1.

METHODS

Human umbilical vein endothelial cells (HUVECs) were treated with dimethyloxalylglycine (DMOG) resulting in HIF-1α accumulation and the FIH-1 protein complexes were pulled down for candidate protein analysis. The metal binding sites were predicted by both MetalDetector V2.0 and Metal Ion-Binding Site Prediction Server, and then the actual ability to bind to Cu in vitro was tested by both Copper-Immobilized metal affinity chromatography (Cu-IMAC) and Isothermal Titration Calorimetry (ITC). Subsequently, subcellular localization was monitored by immunocytochemistry, GFP-fusion protein expression plasmid and Western blotting in the nuclear extract. The interaction of candidate protein with HIF-1α and FIH-1 was validated by Co-Immunoprecipitation (Co-IP). Finally, the effect of candidate protein on the FIH-1 structure and HIF-1α transcriptional activity was analyzed by the InterEvDock3 web server and real-time quantitative RT-PCR.

RESULTS

ATP-binding cassette E1 (ABCE1) was present in the FIH-1 complexes and identified as a leading Cu-binding protein as indicated by a number of possible Cu binding sites. The ability of ABCE1 to bind Cu was demonstrated in vitro. ABCE1 entered the nucleus along with FIH-1 under hypoxic conditions. Protein interaction analysis revealed that ABCE1 prevented FIH-1 to bind iron ions, inhibiting FIH-1 enzymatic activity. ABCE1 silencing suppressed the expression of Cu-dependent HIF-1 target gene BNIP3, not that of Cu-independent IGF-2.

CONCLUSION

The results demonstrate that ABCE1, as a Cu-binding protein, enters the nucleus under hypoxic conditions and inhibits FIH-1degradation of HIF-1α, thus promoting HIF-1 transactivation of angiogenic gene expression.

A comparative analysis of fruit fly and human glutamate dehydrogenases in Drosophila melanogaster sperm development

Glutamate dehydrogenases are enzymes that take part in both amino acid and energy metabolism. Their role is clear in many biological processes, from neuronal function to cancer development. The putative testis-specific Drosophila glutamate dehydrogenase, Bb8, is required for male fertility and the development of mitochondrial derivatives in spermatids. Testis-specific genes are less conserved and could gain new functions, thus raising a question whether Bb8 has retained its original enzymatic activity. We show that while Bb8 displays glutamate dehydrogenase activity, there are significant functional differences between the housekeeping Gdh and the testis-specific Bb8. Both human GLUD1 and GLUD2 can rescue the bb8 ms mutant phenotype, with superior performance by GLUD2. We also tested the role of three conserved amino acids observed in both Bb8 and GLUD2 in Gdh mutants, which showed their importance in the glutamate dehydrogenase function. The findings of our study indicate that Drosophila Bb8 and human GLUD2 could be novel examples of convergent molecular evolution. Furthermore, we investigated the importance of glutamate levels in mitochondrial homeostasis during spermatogenesis by ectopic expression of the mitochondrial glutamate transporter Aralar1, which caused mitochondrial abnormalities in fly spermatids. The data presented in our study offer evidence supporting the significant involvement of glutamate metabolism in sperm development.

The Molecular Docking of MAX Fungal Effectors with Plant HMA Domain-Binding Proteins

Fungal effector proteins are important in mediating disease infections in agriculturally important crops. These secreted small proteins are known to interact with their respective host receptor binding partners in the host, either inside the cells or in the apoplastic space, depending on the localisation of the effector proteins. Consequently, it is important to understand the interactions between fungal effector proteins and their target host receptor binding partners, particularly since this can be used for the selection of potential plant resistance or susceptibility-related proteins that can be applied to the breeding of new cultivars with disease resistance. In this study, molecular docking simulations were used to characterise protein-protein interactions between effector and plant receptors. Benchmarking was undertaken using available experimental structures of effector-host receptor complexes to optimise simulation parameters, which were then used to predict the structures and mediating interactions of effector proteins with host receptor binding partners that have not yet been characterised experimentally. Rigid docking was applied for both the so-called bound and unbound docking of MAX effectors with plant HMA domain protein partners. All bound complexes used for benchmarking were correctly predicted, with 84% being ranked as the top docking pose using the ZDOCK scoring function. In the case of unbound complexes, a minimum of 95% of known residues were predicted to be part of the interacting interface on the host receptor binding partner, and at least 87% of known residues were predicted to be part of the interacting interface on the effector protein. Hydrophobic interactions were found to dominate the formation of effector-plant protein complexes. An optimised set of docking parameters based on the use of ZDOCK and ZRANK scoring functions were established to enable the prediction of near-native docking poses involving different binding interfaces on plant HMA domain proteins. Whilst this study was limited by the availability of the experimentally determined complexed structures of effectors and host receptor binding partners, we demonstrated the potential of molecular docking simulations to predict the likely interactions between effectors and their respective host receptor binding partners. This computational approach may accelerate the process of the discovery of putative interacting plant partners of effector proteins and contribute to effector-assisted marker discovery, thereby supporting the breeding of disease-resistant crops.

Latent generative landscapes as maps of functional diversity in protein sequence space

Variational autoencoders are unsupervised learning models with generative capabilities, when applied to protein data, they classify sequences by phylogeny and generate de novo sequences which preserve statistical properties of protein composition. While previous studies focus on clustering and generative features, here, we evaluate the underlying latent manifold in which sequence information is embedded. To investigate properties of the latent manifold, we utilize direct coupling analysis and a Potts Hamiltonian model to construct a latent generative landscape. We showcase how this landscape captures phylogenetic groupings, functional and fitness properties of several systems including Globins, β-lactamases, ion channels, and transcription factors. We provide support on how the landscape helps us understand the effects of sequence variability observed in experimental data and provides insights on directed and natural protein evolution. We propose that combining generative properties and functional predictive power of variational autoencoders and coevolutionary analysis could be beneficial in applications for protein engineering and design.

Effect Produced by a Cyclooctyne Derivative on Both Infarct Area and Left Ventricular Pressure via Calcium Channel Activation

BACKGROUND

There are reports which indicate that some cyclooctyne derivatives may exert changes in cardiovascular system; however, its molecular mechanism is not very clear.

OBJECTIVE

The aim of this study was to evaluate the biological activity of four cyclooctyne derivatives (compounds 1: to 4: ) produced on infarct area and left ventricular pressure.

METHODS

Biological activity produced by cyclooctyne derivatives on infarct area was determinate using an ischemia/reperfusion injury model. In addition, to characterize the molecular mechanism of this effect, the following strategies were carried out as follows; i) biological activity produced by cyclooctyne derivative (compound 4: ) on either perfusion pressure or left ventricular pressure was evaluated using an isolated rat heart; ii) theoretical interaction of cyclooctyne derivative with calcium channel (1t0j protein surface) using a docking model.

RESULTS

The results showed that cyclooctyne derivative (compound 4: ) decrease infarct area of in a dose-dependent manner compared with compound 1: to 3: . Besides, this cyclooctyne derivative increase both perfusion pressure and left ventricular pressure which was inhibited by nifedipine. Other theoretical data suggests that cyclooctyne derivative could interact with some aminoacid residues (Met₈₃, Ile₈₅, Ser₈₆, Leu₁₀₈, Glu₁₁₄) involved in 1t0j protein surface.

CONCLUSIONS

All these data indicate that cyclooctyne derivative increase left ventricular pressure via calcium channel activation and this phenomenon could be translated as a decrease of infarct area.

Modeling Protein Complexes and Molecular Assemblies Using Computational Methods

Many biological molecules are assembled into supramolecular complexes that are necessary to perform functions in the cell. Better understanding and characterization of these molecular assemblies are thus essential to further elucidate molecular mechanisms and key protein-protein interactions that could be targeted to modulate the protein binding affinity or develop new binders. Experimental access to structural information on these supramolecular assemblies is often hampered by the size of these systems that make their recombinant production and characterization rather difficult. Computational methods combining both structural data, molecular modeling techniques, and sequence coevolution information can thus offer a good alternative to gain access to the structural organization of protein complexes and assemblies. Herein, we present some computational methods to predict structural models of the protein partners, to search for interacting regions using coevolution information, and to build molecular assemblies. The approach is exemplified using a case study to model the succinate-quinone oxidoreductase heterocomplex.

HDOCK update for modeling protein-RNA/DNA complex structures

Protein-nucleic acid interactions are involved in various cellular processes. Therefore, determining the structures of protein-nucleic acid complexes can provide insights into the mechanisms of the interactions and thus guide the rational drug design to modulate these interactions. Due to the high cost and technical difficulties of solving complex structures experimentally, computational modeling such as molecular docking has been playing an important role in the study of molecular interactions. In order to make it easier for researchers to obtain biomolecular complex structures through molecular docking, we developed the HDOCK server for protein-protein and protein-RNA/DNA docking (accessed at http://hdock.phys.hust.edu.cn/). Since its first release in 2017, HDOCK has been widely used in the scientific community. As nucleic acids may include single-stranded (ss) RNA/DNA and double-stranded (ds) RNA/DNA, we now present an updated version of HDOCK, which offers new options for structural modeling of ssRNA, ssDNA, dsRNA, and dsDNA. We hope this update will better help the scientific community solve important biological problems, thereby advancing the field. In this article, we describe the general protocol of HDOCK with emphasis on the new functions on RNA/DNA modeling. Several application examples are also given to illustrate the usage of the new functions.

Mitofusin2 Promotes β Cell Maturation from Mouse Embryonic Stem Cells via Sirt3/Idh2 Activation

β cell dysfunction is the leading cause of diabetes. Adult β cells have matured glucose-stimulated insulin secretion (GSIS), whereas fetal and neonatal β cells are insensitive to glucose and are functionally immature. However, how β cells mature and acquire robust GSIS is not fully understood. Here, we explored the potential regulatory proteins of β cell maturation process and the capacity for GSIS. Combined with the data from public databases, we found that the gene expression of Mitofusin2 (Mfn2) showed an increasing trend from mouse neonatal β cells to mature β cells. Moreover, its protein expression increased during mouse embryonic pancreas development and β cell differentiation from mouse embryonic stem cells. Knocking down Mfn2 reduced Urocortin3 (Ucn3) expression, GSIS, and ATP production in induced β cells, while overexpressing it had the opposite effect. However, neither Mfn2 knockdown nor overexpression affected the differentiation rate of insulin-positive cells. In immature and mature β cells, Mfn2 and its correlated genes were enriched in tricarboxylic acid (TCA) cycle-related pathways. The expressions of Sirtuin 3 (Sirt3) and isocitrate dehydrogenase 2 (NADP+) and mitochondrial (Idh2) were Mfn2-regulated during β cell differentiation. Inhibiting Idh2 or Sirt3 reduced cellular ATP content and insulin secretion levels that increased by Mfn2 overexpression. Thus, Mfn2 modulated the induced β cell GSIS by influencing the TCA cycle through Sirt3/Idh2 activation. We demonstrated that Mfn2 promoted embryonic stem cell-derived β cell maturation via the Sirt3/Idh2 pathway, providing new insights into β cell development. Our data contribute to understanding diabetes pathogenesis and offer potential new targets for β cell regeneration therapies.

The Zip4 protein directly couples meiotic crossover formation to synaptonemal complex assembly

Meiotic recombination is triggered by programmed double-strand breaks (DSBs), a subset of these being repaired as crossovers, promoted by eight evolutionarily conserved proteins, named ZMM. Crossover formation is functionally linked to synaptonemal complex (SC) assembly between homologous chromosomes, but the underlying mechanism is unknown. Here we show that Ecm11, a SC central element protein, localizes on both DSB sites and sites that attach chromatin loops to the chromosome axis, which are the starting points of SC formation, in a way that strictly requires the ZMM protein Zip4. Furthermore, Zip4 directly interacts with Ecm11, and point mutants that specifically abolish this interaction lose Ecm11 binding to chromosomes and exhibit defective SC assembly. This can be partially rescued by artificially tethering interaction-defective Ecm11 to Zip4. Mechanistically, this direct connection ensuring SC assembly from CO sites could be a way for the meiotic cell to shut down further DSB formation once enough recombination sites have been selected for crossovers, thereby preventing excess crossovers. Finally, the mammalian ortholog of Zip4, TEX11, also interacts with the SC central element TEX12, suggesting a general mechanism.

Proteo3Dnet: a web server for the integration of structural information with interactomics data

Proteo3Dnet is a web server dedicated to the analysis of mass spectrometry interactomics experiments. Given a flat list of proteins, its aim is to organize it in terms of structural interactions to provide a clearer overview of the data. This is achieved using three means: (i) the search for interologs with resolved structure available in the protein data bank, including cross-species remote homology search, (ii) the search for possibly weaker interactions mediated through Short Linear Motifs as predicted by ELM-a unique feature of Proteo3Dnet, (iii) the search for protein-protein interactions physically validated in the BioGRID database. The server then compiles this information and returns a graph of the identified interactions and details about the different searches. The graph can be interactively explored to understand the way the core complexes identified could interact. It can also suggest undetected partners to the experimentalists, or specific cases of conditionally exclusive binding. The interest of Proteo3Dnet, previously demonstrated for the difficult cases of the proteasome and pragmin complexes data is, here, illustrated in the context of yeast precursors to the small ribosomal subunits and the smaller interactome of 14-3-3zeta frequent interactors. The Proteo3Dnet web server is accessible at http://bioserv.rpbs.univ-paris-diderot.fr/services/Proteo3Dnet/.